Biofeedback System with Body Mapping Clothing for Patients with Adolescent Idiopathic Scoliosis

Abstract

A biofeedback system for monitoring patient-related signals of a patient having adolescent idiopathic scoliosis (AIS) is disclosed. The present invention provides a personalized biofeedback to the patient based on the patient-related signals for restoring balance in muscle activities and reducing displacement of both sides of the patient's spine as a treatment for AIS. The biofeedback system comprises clothing integrated with posture monitoring sensors, and one or more computational systems comprising a sensor-based sEMG posture training subsystem for providing posture training and a posture monitoring subsystem for providing progressive training through daily activities. The sensor-based sEMG posture training subsystem comprises a posture training device coupled to a plurality of sEMG sensors, a feedback device and a calibration system. The posture monitoring subsystem monitors the posture of the patient and determines the properness of the posture by comparing the real-time data with the calibrated signals from the sEMG posture training subsystem.

Description

FIELD OF THE INVENTION[0001]The present invention relates to a biofeedback system for monitoring patient-related signals of a patient having adolescent idiopathic scoliosis (AIS) and thereby enabling the patient to obtain a dynamic, personalized biofeedback based on the patient-related signals. In particular, the present invention relates to a biofeedback generated from computational systems based on surface electromyographic (sEMG) signals for providing progressive and tailored posture training to patient with AIS.BACKGROUND[0002]Adolescent idiopathic scoliosis (AIS) is a multi-factorial, three-dimensional deformity of the spine and trunk which can appear and sometimes progress during any of the rapid periods of growth in apparently healthy children. Generally, AIS patients are first diagnosed between the ages of 10 to 15 years old with skeletal maturity. Patients with early scoliosis or a Cobb's angle of 10-20 degrees only need to attend regular check-ups every 6 to 12 months. How...

AI Technical Summary

Benefits of technology

[0011]An exemplary embodiment of the present disclosure provides a biofeedback system for monitoring patient-related signals of a patient having adolescent idiopathic scoliosis (AIS), providing a personalized biofeedback to the patient based on the patient-related signals, and providing posture trainings to the patient by restoring a balance in muscle activities and reducing a displacement of both sides of the pati

Problems solved by technology

However, the use of these external supports is limited by factors such as poor appearance, bulkiness, physical constraint, skin irritation, and muscle atrophy that could lead to low acceptance and compliance.

However, patient compliance with the prescribed intervention exercises present a challenge, especially patients who are not self-motivated may not continue with the prescribed exercise programs.

There are many drawbacks regarding to this design.

First, it is intrusive.

This requirement largely affects comfort and compliance of the system, and even causes side effects such as infection.

Second, it relies on a naive feedback loop.

This imposes difficulty in modifying the feedback algorithm once the device is setup.

More importantly, it has intrinsic inability to support adaptation of the feedback logic based either on the historical information such as patient's progress, or on external information such as doctor/specialists' opinion.

Compared to wireless setup, wired design is less flexible, and less comfortable for the patient.

The apparatus sets criteria, which, if not met, may result in a negative reinforcement, such as unpleasant audio tone or, if the criteria are met, will reward the subject.

Even though this design considered the aspect of adaptation, the adaptation method it used is very primitive—it is achieved by adjusting criteria upwards or downwards.

In applications, however, the criteria are hard to set because multiple metrics (resulting to multitude of criteria) should be considered, let alone each criterion should vary from patient to patient.

Hence, simply using criterion-based detection in this scenario is not sufficient.

Another drawback

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